GDPR & CCPA For Apps – Tips For Privacy Compliant Apps

Let’s look at GDPR, the CCPA and how you can make sure that your app is ready for the coming changes.

What’s the most important currency around? It’s data. It’s used to fuel everything from your personal virtual assistant to your social media feed. But let me tell you one thing about this data. It’s private, it needs to be safeguarded and soon, fellow app developers, it will be the law for you to ensure this.

Data is so omnipotent in our digital lives. Privacy regulation is set to make data handlers liable for how they collect, protect, store and remove this data. Some have predicted that up to 55% of apps aren’t ready for this change.

But you thought GDPR is only for email marketers. Wrong. Complying with privacy regulations is integral to running a successful mobile app business. As a mobile developer, under the new legislation, you will be responsible for all the personal data from your app.

That’s right – as of the 1st Jan 2020 responsibility will rest with you to ensure that you are in control of user data. But it doesn’t have to be all doom and gloom. The GDPR and CCPA are an opportunity for developers to create effective relationships with their users. It also means that you can offer up a great app experience at the same time.


But what is GDPR and CCPA?

GDPR stands for the General Data Protection Regulation and it came into effect on the 25th of May 2018. It is designed to protect data as it is collected and stored. It is also in place to ensure that the user is in control of their data. It seeks to allows the user to easily opt-out and remove their data when they so desire.

The CCPA is similar and will come into play on the 1st of Jan 2020 – the California Consumer Privacy Act is a bill meant to enhance privacy rights and consumer protection for residents of California, United States.

For apps, this means that a proper system for opt-in, data collection and data storage will need to be in place. As well as this the infrastructure to opt-out and be forgotten are essential to comply with the legislation.

There are some key principles to define when looking at the legislation from a developer’s perspective. We will help to explain these next and look at exactly what these principles mean for developers, as well as practical advice for app owners.

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Explicit consent

This is a key requirement for mobile apps. The legislation says that businesses must request and receive consent to collect use and move personal data. Further, this request must be made and given in clear intelligible and easily accessible way. It cannot be confusing. As well as this the user must be able to withdraw consent as quickly as they can give it.

This means that apps will need to communicate better with their users. They must clearly define the type of personal data they collect around users. Developers will need to explain why this data is collected and obtain clear consent to collect this information.

Practically this means that you may wish to ask for certain types of personal data at different points of the user experience. For example, it’s generally a better idea to ask users for data consent at a point where it is relevant to the action that the user is performing.

So don’t ask for every permission under the sun the first time your app is opened. It might be better to wait for the right moment to communicate these to the user.

This also gives you a better opportunity to communicate the value that the user will receive by opting-in for this type of data collection. It also means that you can clearly explain opt-out procedures as well (but more on that later).

For example, we help our partner apps to obtain consent for location permissions by providing a dialogue with the user at the right moment. This could be when the user is looking for nearby venues or searching for local deals.

By clearly explaining to the user at this moment it allows the user to come to an informed decision on how they want to share their personal data with the app. This complies with the ‘explicit consent’ as defined in the GDPR legislation.

Find out more about asking for consent by speaking to our app team.


The right to be forgotten

One of the keys focuses of the legislation is the right to be forgotten. This means that app developers will need to create a system of opting-out that allows users to be in control of the data collected through the app.

As previously mentioned this should be as simple for the user as opting-in. Your app users should be able to request that their entire data history is deleted and removed from all records. This includes third parties (yes that means every SDK that you have used in your app that uses personal data).

For developers, this means designing user control into the app so that the user can perform these actions when desired. Apps must be able to process and act upon these user requests and then ensure that all personal data is removed.

This might be in the form of an option to contact you with questions about your data.

Or you can add a data section to your app settings page that allows your users to opt out of different types of data collection. You can also add the option to revoke all data collection.

The aim of GDPR in this area is the put the user in control of their data. If you can design your app to facilitate this control then your app will be compliant and your users will have a better experience when using your app.


Privacy by design

This section is all about the proper encryption and data handling procedures.

You might think that this is an obvious approach to take when designing a mobile app. Perhaps you have considered privacy at multiple points in the planning of your app. That’s great – the key points to remember is that GDPR makes this a legal requirement.

So from a project’s inception to every point in the lifecycle privacy and data protection will need to be front and centre. It’s about anticipating, managing and preventing privacy issues. And doing this before a single line of code has been written.

There are fundamentals that app developers will do well to follow once the legislation comes into force:

Privacy must be proactive, not reactive, it must also be preventative not remedial. This means that developers should be thinking about privacy from stage one of the design process all the way through to after the user’s app engagement has ended.

Define the kinds of data that your app will use in the design phase. Assess potential issues that may arise when using this data. Make sure that your app is designed to secure this data by default and has the correct opt-in processes before you do anything with this data.

When processing user data ensure that your systems are designed to secure the data. This might mean pseudonymization of data or even creating a completely secure way of processing personal data.

The basic idea here is that privacy and data control to become a key part of designing any new app feature. By taking this approach you create an app experience that is secure. It provides users with the controls to input personal information in the knowledge that it is secured and that they can have it removed at any time.


Consent module and Tamoco’s secure SDK

As mentioned one area where developers need to ensure compliance with GDPR is through the use of third-party SDKs. Many of this access and use user data, and often there is not explicit consent for this from the end user.

If you’ve been paying attention you’ll realise that this is a direct breach of GDPR. As a developer, you’ll need to balance the use of third-party SDKs with user privacy and consent. Partnering with SDKs that place user opt-in front and centre will be a sensible approach once GDPR comes into effect.

At Tamoco we help apps to comply with the new regulation whilst providing a powerful toolkit to boost app engagement and monetization. Our product allows apps to get valuable insights and analytics into their app audiences whilst ensuring GDPR compliance.

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Five Key Mobile App Statistics App Developers Should Know

Sure there’s yearly reports on everything from app usage to revenue. We welcome these and they can provide developers with vital information about the app economy. But often it can be difficult to understand how these trends will affect your app.

So we’ve tried to help. We’re going to look at five interesting stats based on data from the last year. Then we’re going to attempt to understand what these trends show, how it will affect monetizationengagement and other app metrics. We’ll also look at how developers can adopt their app strategy to suit these trends.


Last year app mobile device app downloads reached over 175 billion

This represents a 60% growth on 2015. Now that’s healthy, and there are a few reasons for this.

Firstly, more apps are free to use or try and more developers are finding this model attractive. For the consumer this means that apps are free to download. With the rise in subscription models and other post-download monetization options, this is greta news for publishers too.

The number of smart phones in circulation has increased, especially in emerging markets. Rapid mobile adoption shows that there is still huge potential for app growth.

Mobile devices now have much better storage options. Users previously had to manage device storage carefully. The lowest storage bracket on newer devices has increased and cheaper cloud options leave a lot more space on devices for apps that would have previously taken up too much space.

Finally, people are prioritising mobile to complete tasks that would have usually been difficult on a mobile device. Apps are now much more secure and user-friendly. This makes tasks like shopping or managing finances much easier.


What can developers learn from this?

You should think about making your app free to download and monetizing after the app experience. Users increasingly expect apps to be free.

Whilst it’s still important to keep the size of your app as low as possible, this isn’t as much of an obstacle as before. Instead users are looking for apps that help them to achieve tasks on their mobile. Positive user experience is important for users. They want to be able to do powerful things in a great app experience, without having to open their laptop.


Consumer spend exceeded $86 billion

When we look at the total spend by users the figures make for positive reading. This growth remains strong thanks to the increase in smartphone adoption in the developing world. The ability for publishers to capture more revenue from their users should not be overlooked.

In terms of the app store, app revenue is still higher in iOS than Google play. Worldwide gross app revenue reached $38.5bn from the app store in 2017 compared to $20.1bn from the Google play store.

This shows that Apple products do continue to attract, on average a user that is is more likely to part with cash via apps. However, both stores showed similar revenue growth levels of around 35%.

The consistent growth suggests that publishers are successful implementing monetization strategies. This is allowing them to generate more revenue per user. This may include subscriptions and freemium etc.

Developers will be happy to see that monetization in top markets maintained a steep growth – 70% in the US and 35% in the UK. But the real story of the last year in terms of app development is the scale of growth in developing markets.

The short story is this – the app economy is in a great place right now. Consumer spend has doubled in 2017. Publishers will need to look at their monetization strategy in developed markets. Here they will need to balance experience with monetization. As well as this they should be looking at new ways to monetize without choosing an advertising solution.


App store consumer spend in China grew by 270% in one year

App store spend is growing at a much faster rate in emerging economies.

China and emerging markets are fantastic examples of where developers should be looking in terms of app monetization. In the last year apps are becoming widely used in citizens’ daily lives. Much in the same way that apps have revolutionised other lifestyles, the same is happening in emerging markets. This is because more people are using mobile devices to perform daily tasks.

Rapid growth in downloads across other developing nations will provide even more opportunity for growth.

There now exists a lag between the number of downloads in these emerging markets and the equivalent revenue for app developers. The potential for monetization is huge. Publishers need to move to make sure they can tap into one of the biggest monetization opportunities out there.

Add to this that India and Brazil are areas where app usage is also increasing at at an alarming rate. India is now in second place globally in terms of number of app downloads. In these economies Android devices are more popular. This means that ensuring you can support both platforms could be the key to sustained growth.


What does this all mean for developers?

Firstly, we can still conclude that the average iOS user is worth more than an Android user in terms of monetization potential. But growth is steady across both OS.

The success of publishers monetizing after the point of purchase continues to drive revenue in developed markets. Subscription models and other models allow time for the publisher to educate and engage users on their apps value. This encourages better monetization. Ads are still a strong source of revenue for apps. But apps as a service are increasing in number and developers are getting good results from this monetization model.

In developed markets app discovery is becoming more difficult. But, the potential for revenue through monetization after the point of download is increasing.

Mobile apps are dramatically increasing in the developing world. The rapid number of new device adoption means a huge amount of new users. The value of these users is still low compared to developed markets. But, this still represents a huge opportunity for revenue growth.


Each mobile user spent 1.5 months in apps

It’s safe to say that users are spending more of their time in apps. And it’s also pretty certain that users are using more apps, on average. Last year users spent on average over 3 hours a day in mobile apps.

This presents far more opportunities for developers to create effective engagement strategies. Users want to complete more tasks on a mobile device and they love to be able to do this in apps.

Improving lifetime value and customer satisfaction is a crucial part of creating a successful app. Being able to engage apps leads to better monetization and more chance of increasing your user base quickly.

There are two things going on here. In developed markets, users are doing much more on their phones. But in emerging markets users have skipped the use of a desktop and see mobile as an effective way to complete certain tasks for the first time.

The time is now for developers to put experience centre of their app strategy. Their app solution should take advantage of the increased amount of tasks that users are doing on mobile. In some ways engagement is more important than downloads – if you can’t keep users in your app then you’ll churn users and very quickly have a worthless app. These figures show that users want positive experiences and the ability to complete their goals inside apps – developers should focus on delivering this.


The average smartphone user accessed around 40 apps per month

More tasks than ever are being completed on mobile.

You might think that all that time is being spent on the bigger apps. This is simply not the case. Users are looking to apps to perform a variety of tasks that can only be achieved by a single app. Users are looking for powerful apps in each category and they are choosing the ones with the best experience and best tools for the job in question.

Engagement is of course important for monetization. Keeping users engaged and happy is key to generating high revenue. That’s why these stats are promising for developers. If you can successfully implement a great engagement strategy you will be able to monetize effectively.

As app attention grows publishers will need to understand what this means for their app. This could mean focusing on UX and understanding how improving this will mean more engaged users. Or it could mean focusing on how push notifications can improve app retention.


What does this mean for developers?

As users spend more time in apps and use apps to solve problems and complete tasks developers will need to seize the opportunity and ensure that their app offers a seamless user experience.

Experience is key to successful monetization. Publishers that are looking to increase revenue, especially in emerging economies will need to focus on retaining their users.

The stats say that users are spending more time in apps, but they won’t just choose any old app to reach their goals. Apps still need to be powerful and they still need to have a great experience to attract and retain users.

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How Big Data & Location Intelligence Is Changing The World

There’s no doubt that the explosive rise in the number of smartphones has changed the world as we know it. The increased number of sensors and connected devices has produced mountains of data. This is being used to transform the way that we live our lives.

IoT, location data, location intelligence, big data. Whatever your name for it, it’s hard to dispute the potential across a variety of industries

It’s now apparent that granular location data can provide unprecedented insight into the offline world. More businesses are realising the value of mobile location data and the impact it is having across the globe.

As we move away from unreliable data sets, sensor-driven accurate data sets are taking centre stage. This kind of accurate data has many applications. But I’d like to look at some that are having the greatest disruptive impact.

Business intelligence

The ability to notice trends by using data isn’t new. The ability to do this based on people’s activity in the offline world, in a close to real-time manner is.

Location intelligence reveals relationships between big data sets that often would be missed. It turns these insights into actionable business intelligence. Helping inform decisions, from the boardroom to the storefront.

From the small bar that is competing with huge chains of venues through to the small retailer competing with online mega corporations. These businesses are gaining valuable insights from using this kind of big data to inform their business strategy.

The truth is that mobile location data has now matured enough to solve many problems that both small business and enterprise face. Let’s look at a few:

Financial services – understanding footfall through big data sets is valuable for the financial sector. Mobile device data can help to forecast earnings and other KPIs before they are formally reported. This helps inform investment decisions.

Retail – big data can help both small and large retailers. Understanding store visits, as well as customer behaviour through mobile device data, is having many positive effects on the retail sector. These insights can help inform business decisions such as store layout, opening times, staffing and more.


Infrastructure and planning

We’ve all heard of the term smart city. We like to think that there’s more to it than just adding a few data points and putting the word smart in front of it. It is, in fact, more than that. We’re moving towards urban centres with huge populations and aspiring towards self-driving vehicles. Big data is the key to unlocking this truly smart future.

The rise in mobile device data has provided better opportunities to understand how cities work.  It’s helping to create systems and infrastructure that reflects this.

Combined with the increasing number of connected devices in cities, central planning authorities now have a set of tools that can inform decision making in many different areas.

Mobile location data is contributing to a better understanding of where demand for public infrastructure is greatest. For example, examining mobile device location data to understand the most cycled roads within a city. This information is precise and invaluable when planning where to implement new routes.

The same is true of traffic and congestion. In increasingly crowded and polluted megacities, it’s important to understand how traffic issues can be alleviated. Understanding traffic flow and where to build new road structures, or introduce new low emission zones is crucial to building the kind of smart city that can sustain current levels of population growth.

Big data is having a huge positive effect on this kind of planning. Thanks to the accuracy and uniqueness of mobile device data and location intelligence, it is changing how decisions are made in cities and towns around the world.

Marketing and advertising

Big data and marketing have always complimented each other. Marketers have always looked to use data sets to improve the efficiency and effect of ads. Using big data to create tailored and relevant audiences is not a new practice.

But mobile location data allows marketers and advertisers to connect digital advertising to how consumers behave when they are offline. Understanding how consumers move in the offline world is helping marketers to become more effective. It’s assisting marketers in providing more personal advertising to consumers.

Location intelligence is disrupting many stages of the consumer lifecycle. It’s bringing the analytical capabilities that have been available for the web to the real world.



Mobile device data is helping to build up complex pictures of how people move and behave. This helps advertisers to build complex customer profiles. Brands are finally understanding the places that their customers go and how they interact with the physical world around them.

This is far more effective than other methods of audience segmentation. This is because a person’s location is often a much greater sign of intent than when they are searching for something on a computer, or browsing on their phone whilst sat on the couch.

This allows marketers to identify exactly where consumers are on the buyer journey. Moreover, it allows them to do this with a greater level of detail.



One big breakthrough that big data has had on marketing and advertising is by increasing the ability personalise at scale.

Location data is allowing brands to be helpful and human by understanding the situation of the customer. The concept isn’t new, but the accuracy and increasing size of data sets in the space have allowed commutation to really get personal.

Location help provide promotions at the moment when the customer can actually redeem it. It allows the ‘customers also bought’ experience to reach the real world retail store. In this way, big data is providing digital solutions to offline problems. Location intelligence is tailoring brand communications to a person’s unique experience of the real world.


Customer experience

Big data has changed customer experience for the better. Location intelligence can help to automate way-finding, ordering, assistance and queue management. Understanding the physical location of a person has helped improve the guest experience across many sectors.

Stadiums, resorts, airports, transport hubs all stand to improve the experience of the people who spend time and money in these places. It might be location based ticketing – you buy your ticket by walking onto a train. Or it may be ordering food and drink to your location.

There’s still huge scope for big data and location intelligence to be applied to improve the customer experience.



Mobile device data as we have seen has connected many digital walks of life to offline consumer behaviour. Another way that this technology is revolutionising the marketing and advertising space is through attribution.

Traditionally many advertisers have been blind when it came to measuring the impact of offline ads on offline KPIs.

But the mobile device location data is filling in the blanks. Location intelligence can understand when a person is in front of, for example, OOH advertising. It can then measure how many of these people are then seen inside a store or in front of a specific physical product.

Connecting the two provides an accurate way for marketers to measure the impact and ROI of offline advertising inventory. it also allows them to measure the effect of digital advertising on an offline goal. These things have just not been possible with certainty before. But big data has changed the way that advertising can be measured.



If AR is really going to live up to its promises, it will have to rely on complex data sets and accurate location intelligence.

As AR gains prominence it’s application will move beyond fun to play games to useful productivity applications (you can even combine it with some powerful notion templates to really level up). As AR develops, it’s used as a way of reaching audiences with content and advertising will grow. Like previous marketing activity, it will be improved by the use of big data and location intelligence.

AR will require huge amounts of accurate and real-time location data to function properly as a user moves around the real world.

Optimising the supply chain

Big data and location intelligence is impacting organisations that want to optimise the supply chain.

The obvious application of location intelligence in the retail supply chain lies in the ability to understand and track deliveries and supplies. It already being used to generate data sets which can optimise and improve these services.

But location intelligence isn’t just helping business to optimise the process. It’s helping to understand the demand for products. There’s a lot of history of people building something in the hope that people want it to then find out that actually, they don’t.

Another way that big data is helping manufacturing industry to optimise is by helping it to adjust the type of transportation, pickup location or place of sale.

With the rise of location data, these insights are now fuelled by information from the offline world. Insights that have previously been unattainable or have lagged are now available in real-time. This lies at the heart of what is disrupting how the supply chain operates.


Privacy and transparency

As usual with new disrupting activity, the focus in on the responsibility of these new technologies. And rightly so. Indeed those in the big data space will need to be more transparent in how data sets are sourced.

It won’t be enough to simply check a box and start collecting and aggregating personal data. More needs to be done in order to clean up the data supply chain. More control needs to be handed to the user.

In this way, it’s our responsibility to communicate the value of big data and location intelligence to the user. It’s having a huge positive impact across the globe, and that’s just more reason to get the privacy part right.

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Learn more about big data

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Marketing & Advertising

Mobile Targeting – Get Programmatic & Social Right With Data

There have been issues with data accuracy in mobile targeting in the past. Targeting the right person in the best moment is still the appropriate goal for marketers. But to do this effectively, the data that fuels campaigns must be reliable.

If marketers don’t work from reliable data sets then the information is useless, and mobile targeting will be more of the same.

Today, the availability of scalable first-party data sets is there. Brands and advertisers need to be able to understand what good data should look like.

We’ll look at a few fundamentals to look for that will ensure your mobile targeting campaigns are powered by quality data. We’ll also discuss the effect this will have on different mobile targeting channels.


What is quality location data?

When we talk about data we look for the following attributes:

  • First-party – is the data from a first party source. Second-hand data that is unverifiable is not helpful as it could be inaccurate or out of date.
  • Sensor-driven – this means that the data sets are sourced from accurate sensors. Precise and reliable data sets are sourced from multiple sensor types to ensure accuracy and scale.
  • Real-time – Datasets must be immediate in order to verify accuracy. To achieve effective personalisation and mobile targeting, action must be taken based on data sets that are real-time.


Programmatic advertising & location data

Location is a fantastic trigger to help fuel mobile marketing campaigns. That is if you can get the moment right.

Programmatic has always had its problems – automation is difficult to get right. A lot has been said about programmatic and it’s effect on delivering relevant content to the right person at the right time.

We’ve all seen and remember poor individual use cases of programmatic advertising. Mobile programmatic targeting has taken the plunge and aims to put data at the centre.

However, problems of accuracy remain if the data that is being used to fuel programmatic advertising isn’t accurate.

Location-based marketing and programmatic are effective when the following conditions are met:

  • Mobile targeting can be achieved in real-time
  • Data is derived from accurate, sensor-driven networks
  • The data is first-party

Without this programmatic mobile targeting will be ineffective. Outdated data sets can mean that you completely miss the relevant moment to target audiences.

It can also mean that your attempts to personalize the experience miss the mark. We all know how important personalization is to the modern marketer.

There are a lot of companies that claim to provide accurate data, but these are rarely meeting the three conditions we just discussed.

Often the data is third-party, it’s driven not by accurate sensors, but vague lat/long indicators. It’s often not live or real-time either.

So make sure your data partner can deliver on those three points. This will allow your programmatic mobile targeting to truly feel personal. It’s also important to get a good understanding of what stream processing is.


Location-based social media marketing

With organic social media becoming more obsolete, more brands are looking at increasing their ad spend on social to ensure that they reach audiences constantly. One way to do so is by using social media management tools that automate posting regularly on all social channels for maximum reach.

Social targeting options allow for geographic targeting.  But the accuracy of these targeting options is yet to be verified. How do you know you aren’t targeting a user who checked in there over a week ago?

Let’s analyse current social media mobile targeting options in relation to our three commandments.

Real-time – Facebook’s geographical targeting feature is rather vague when talking about its geo-targeting options. Or at least when talking about the speed of the targeting. “people recently in this location” is how they describe it. But there’s little in the way of how recent.

Now, this is useful for some advertisers, but to create a truly personalized and real-time experience, it has to be instant.

Accurate sensor-driven – Again it’s hard to tell exactly how Facebook sources its location data. We suspect that a large proportion of data is derived from check-ins on the Facebook platform. This does raise some issues – it relies on the user selecting the right location, for example.

Social is a powerful channel for targeting users. But the potential is even greater if brands can accurately target users in real-time. It’s even more effective if this is done in relevant locations and with personalized content. This can even be leveraged to create social proof.

In order to achieve that, the data that fuels social targeting and retargeting needs to be accurate.

Social platforms have always focused on personalization of the news feed. But this highlights some of the problems with facebook ads – they aren’t relevant.

Facebook has spent so much time personalizing the organic news feed but then delivers any ad at any time.

This means that brands and advertisers who embark on Instagram marketing can ensure that relevant, in the moment ads, reach users using Instagram stories data will be on to a winning formula.


Post mobile targeting attribution

The same can be applied to attribution. Post mobile targeting attribution is valuable for advertisers and marketers. It’s important to measure attribution, especially in the offline world. Mobile location data has been instrumental in this. Closing the online to offline attribution loop is now possible thanks to device location data.

But again, to truly understand physical conversions, marketers need accurate and real-time data sets.



Mobile targeting is a powerful tool for marketers to reach users with personalised messages, at the right moment. Location data and location intelligence helps provide the context that mobile targeting takes place.

Whether programmatic or social, mobile targeting requires data. This data must be accurate, real-time and first-party to ensure that location-based mobile marketing is effective.

Precise data is now available at scale. This means that marketers now have a powerful tool at their disposal, as long as they utilize the right data sets.

Marketing & Advertising

The Evolution Of Location Based Marketing & Advertising

Let’s get stuck in with a definition.

Location-based marketing is the practice of using physical location to inform and optimize advertising, communication, targeting, loyalty and attribution.

This sometimes also known as location-based advertising or proximity marketing. At the most basic level, it means creating a one-to-one relationship with the customer. Emphasis is placed on communication in the right place, at the best time and with the relevant message.


A history of location-based marketing

Despite popular opinion, location-based advertising has been around for a while. Sure, it hasn’t always been backed by the smart technology in your mobile phone. Buy it has existed in some form. Local marketing strategies have been a key part of marketing since the practice began.

It might not have been as effective, but brands have been trying to target users based on location since well before most of you reading this were born. Advertising space has always been purchased based on its location. Be it a metro station in an affluent Paris Arrondissement. Or a teenager holding a sign advertising bagels in a certain street in NYC.

Location-based marketing has developed a lot since then. The underlying technology has advanced at an alarming rate. The ability to understand where audiences go and the ability to market to these is improved.


IP addresses and targeting over Wi-Fi

Location-based marketing has always been around. But, it did up its game in the 90’s once the internet found its way into most family homes.

The dial-up broadband revolution had begun. Most families now had internet access in their home through (a large) desktop computer. For marketers, this meant that advertising could be delivered to a user based on their IP address. Thus marketers could now target based on location, usually an area the size of a postal area.

There are a few problems though. Unless you have a very specific audience in a single geographical area, it’s difficult to ensure that you are targeting the right people. By today’s marketing standards, an entire postal area is just not specific enough.

Another problem that remains is the lack of ability to understand who is on the other end of the screen. When IP targeting was becoming popular, many families had a single computer. The whole family used this. Dad to research which TV to buy next. Mum to book the family holiday. Kids to play online games. This means that ad dollars could easily be wasted. As advertisers were under the impression that they were delivering highly targeted ads.


The beginnings of mobile targeting

Location-based mobile advertising was the next advance in location tech. Phones become truly mobile in the early 2000’s. They were used by the masses and even your gran had one. This meant two things:

It was now possible to advertise to an individual based on physical location whilst they were on the move.

Location-based marketing companies now had a means of segmentation down to a single person.

This was fantastic and all, but the main issues were still accuracy. Whilst the ability to target an individual on the move was well received, issues still remained. The area in which location could be accurately placed was still too large. Mobile targeting via phone masts was a vital step on the road to personalization. However, those in pursuit of accuracy were still frustrated. It often requires a form of phone validation to ensure the right person is reached.


GPS, geofencing and geotargeting

In the late 2000’s smartphones really took off. The first smart devices appeared and with the first iPhone, the race to create powerful mobile devices went mainstream. This led to rapid growth of mobile device adoption with GPS capability.

This changed mobile targeting for good. It was now possible to use a device’s precise GPS satellite positioning to understand device location. This process is known as geofencing, geomarketing, or geotargeting.

A geofence is a virtual boundary that is defined in order to perform a specific response once a device enters or leaves the defined area. More advanced geofencing is possible. An example is focusing on dwell time. Triggering a response when a device is within a geofence for a minimum amount of time.

The geofence allows for mobile targeting to occur on an individual level anywhere. It means that audience segmentation can occur based on individual movement. But it did more than previously possible. It was now far more accurate. This meant that personalization (relevant to location) was now available. Location-based marketing was now personal and precise.


Satellite problems and indoor confusion.

Location-based mobile targeting improved in accuracy with the geofence. However, on a precision basis, it’s still not perfect.

GPS has issues in some indoor spaces. So it might be incredibly effective at understanding where a person is in their car whilst driving. As soon as they enter an indoor space, GPS can be temperamental.

Some may argue that this level of detail is neither here nor there. But the problem arises when location data providers can’t differentiate between accurate data and inaccurate data. Advertisers and marketers don’t know when the data fuelling their campaigns is incorrect.


Beacons – iBeacon and Eddystone

You thought that it was location-based marketing you were doing before? How very wrong you were. Beacon adoption was a trend that changed location-based marketing and advertising even further.

Thanks to mobile devices and mobile targeting, advertisers would now focus all efforts on accuracy.

Beacons were the natural next step in this journey. Beacons are small Bluetooth devices that can interact with a mobile device. This interaction allows the device to understand exactly where the mobile device is with an accuracy down to 1 meter. Not only does it do this, it can measure positioning on a vertical basis.

There are two main types of beacon technology; iBeacon and Eddystone. These devices are deployed everywhere from shopping malls to sports stadiums.

This was the true beginning of proximity marketing. Delivering personalizable content with accuracy in precise micro-moments. These moments are relevant to the time, place and person. Beacons also allowed for accurate attribution in the offline world.


But what about scalability?

The problem with Beacons is that they require hardware to be deployed. Unlike Geofencing, where geofences can be set around any place that satellites can reach. Beacons must be physically deployed inside a store, or inside a football stadium. So these individual businesses can enjoy a high level of location-based marketing. Once a person leaves these areas they cannot get the same level of location-based insights.


Enter the network approach

The solution to this problem? The proximity network approach. To maintain beacon based, accurate levels across entire cities, it’s essential to collate this location based hardware. That’s what Tamoco’s proximity network is. It’s a complex network of location-based sensors. This allows for mobile targeting, location-based, marketing and location intelligence and insights at a consistently high level. Moreover, this is available whether the device is indoors, outside, on the first floor and so on.

One issue that many proximity marketing companies have had with location-based advertising is with scalability. Creating a proximity network of location allows for this scalability.


Sensory agnostic and a view of the future?

At Tamoco we take a sensory agnostic approach to location-based marketing. We believe that as technology advances, we need to able to adapt to the changing was that advertising, targeting and marketing will change.

A good example of this is the emerging tech around AR. An AR headset or device is still a sensor. Location is a means to an end for targeting through this medium.

By taking an agnostic approach and ensuring our network approach, we ensure that advertising and marketing can keep up with the cutting edge trends. Moreover, we can ensure that this is available with a precision and scale that those original metro advertisers could only dream of. Using location to improve marketing is here to stay.

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App Monetization Is About The App Experience – Here’s How

Is there a point where too much monetization can have a negative effect on the app experience? It’s more important than ever that mobile app developers understand the effect of app monetization.

Across app monetization strategies there are some mistakes that can have a negative effect on user experience. In these cases, it will cause you to lose valuable users. 

Are in-app ads damaging the app experience?

It’s amazing that so many developers fail to see this. Poorly implemented in-app advertising is one of the worst forms of mobile app monetization. It might be an obvious thing to say – but users don’t like ads. An app monetization strategy should be carefully managed and well devised. A mobile app monetization strategy that consists of ads can be successful. But when the ads are poorly implemented, they start to have a negative effect on user experience.

Protecting the user experience means taking care and understanding the effect of in-app ads. Too many intrusive and you’ll begin to lose those users that you’ve spent valuable time and money acquiring.

Typically the best ad formats for in-app monetization are native, interstitial and incentivized advertising. It’s important that the ad feels like it was designed to be in the app, not forced in at every opportunity. It’s also important that you think through where your ads appear. Do you have a clear idea of the best user experience? Ensure that your ads don’t affect this flow. Failure to understand this will lead to a negative user experience, and cause you to lose users.

App monetization is about striking the right balance between revenue generating strategies and improving the user experience. In terms of ads, content is often overlooked. Your ad content should be relevant to the user. There are many tools to ensure this, but one way to do this is to engage with affiliate sponsors. Striking a more bespoke advertisement agreement will allow you to choose the ad content. Affiliate ads on mobile generally take the form of another app. This ensures that the content is relevant. You can even offer affiliate advertisers an ad spot for one in return.


This app monetization strategy has become very popular withe the decrease in paid apps. With freemium, in-app monetization is about building a large base of users. Make sit clear up front that your users will only be able to access certain features without paying. Failure to do so will lead to some unpleasant app experiences.

Be transparent – it will be helpful in the long run. With this app business model, you’ll see that a small number of users will contribute a huge amount of revenue. In games, this type of user is generally someone who wants to advance throughout the game faster. Or unlock features that usually would take a regular user a significant amount of time. It is therefore important that you continue to generate and maintain the users that don’t pay anything. These free users are important as without they there would be no reason for paid users to continue paying for extras.

You must strike the right balance – between free features and paid features. If you get it wrong you’ll lose users. But of course, you also need to entice users to upgrade

In terms of experience, you can try educating the user better. Helpful, intuitive, experience first monetization is the solution. Also, don’t let your app become the next news story about a child spending millions on added content. We don’t need to tell you that’s not a positive app experience.


Subscription model

Another model that relies on experience first app monetization is the subscription model. By placing the experience first, app owners will produce better user retention and engagement. It’s simple math to understand that the more users on your app, the more that will enter into a paid subscription.

But placing experience first will also allow you to increase the percentage of users that enter into a subscription. Focus on creating improved UI and increasing user satisfaction. This will increase the number of users that subscribe to your service.

Don’t over confuse your options – users will become tired and move on. With more app subscriptions resembling SaaS services, make sure that your strategy cuts through the noise. Focus on an attractive user experience to maximize upgrades. Focus on simplicity when explaining the benefits of subscribing.

Rather than only focusing on converting new users into subscribers, remember to listen to your current subscribers. What are their complaints? What are the features they want? Many developers think (incorrectly) that once they have a paid subscriber they have one for life. In truth app experience is just as important after the moment of subscription as it is before.

Data monetization

Most mobile app monetization strategies will have some kind of negative effect on the app experience. The exception to this is data monetization. By running in the background, app developers can generate large CPMs from their audience without having a negative effect on the user experience.

This opens up a wider debate on the nature of app monetization. It’s important that users realise the tradeoff between experience and revenue model. When using this app monetization strategy developers must recognise user privacy. They must also be able to communicate why apps are free. It’s important that developers and users engage in debate around the benefits of free apps. Users must also understand the reasons for this.

Other in-app monetization advice

To protect the user experience and maximise app monetization make sure that you don’t make any of the following mistakes.

Make sure that your app monetization strategy works across platforms. If your app exists on multiple mobile platforms then make sure that your strategy is adapted to each. This could be as simple as optimizing ad formats on different screen sizes. It could mean that you’ll need to utilize a completely different app monetization model. The main rule is to understand the audience across platforms. Then adapt your app monetization strategy accordingly.

Use analytics. One of the most important things that developers can incorporate into app monetization is data. Use data generated from monetization to understand your progress. There are plenty of tools to help developers understand app engagement and app retention. Combine this with an app monetization platform that can give you accurate insights on how app engagement can affect revenue. Connecting the two is key to succeeding at in-app monetization.

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Marketing & Advertising

Cross-Device Attribution, Location & The Customer Journey

Marketing attribution has always been a tough area for marketers and advertisers. Attribution modelling has undoubtedly provided huge value. However, the ability to measure the effect of channels (or touchpoints) on the customer journey has often been fraught with difficulties.

Return on investment has been difficult for a variety of reasons. Understanding the entire customer journey has been problematic. This is especially true in the physical, offline world. Some offline marketing channels have also not been trackable. Attribution solutions have struggled with the multi-channel and real-time aspects that are crucial to understanding the full marketing picture.

Location intelligence has grown in terms of accuracy and scalability. This presents an opportunity. Some of the problems with attribution modelling can be solved with the application of accurate location data. Could this be the solution to the problems that limited marketing attribution in the past?


What is marketing attribution?

To understand the problems and the effect that location data can have it’s important to understand marketing attribution.

Attribution is the practice of allocating purchase revenue to the marketing touch points of a customer. In other worlds – understanding the effect of marketing efforts and channels on the purchase decision of customers.

Touchpoints can cover a wide range of customer interactions. Understanding the effect of these on sales or other valuable metrics allows for the optimisation of marketing channels, activity and budget.


What is offline location-based attribution?

Location-based attribution is the use of accurate mobile device data to fill in the gaps in traditional attribution models.

Smartphone adoption has grown rapidly. Understanding the where and how people move becomes scalable and precise. Customers rarely move without their mobile device, and this is the key. Higher levels of attribution precision are possible. Connecting the online and offline worlds becomes easier. Customer journey mapping and various touch point measurement is improved.

Until recently it has been impossible to understand the offline world. This has meant that advertisers have often been unable to attribute sales in physical stores and locations to a specific channel.

As smartphone adoption has grown using a device location has proved extremely useful in connecting the two.

Mapping the customer journey – cross device attribution

Basic attribution models have chosen to measure first touch or last touch. Much has been written about the failings of each. The choice lies in ignoring either early, top of funnel activity. Or failing to consider later, bottom of funnel activity that helps to move the customer along the buyer journey.

So the natural next step is to focus on multi-touch attribution. Focusing on touchpoints throughout the customer journey is important. But it requires accurate measurement across channels to be effective. The problem is that multi-touch attribution models don’t always incorporate what is happening in the offline world.

Location data allows a complete understanding of the customer journey. This means that it becomes possible for businesses to say the sort of thing like – okay this person saw our Facebook ad and has now completed a purchase. Previous attribution models would then attribute this purchase to the Facebook ad and not demonstrate how to generate leads on Facebook. But a more holistic view of the individual customer might point out that actually, the customer had visited the physical store previously.

This ability to model attribution across the online and the offline leads to a clearer picture of attribution. It allows brands to be better informed about the effect digital has on physical and vice-versa. Your customers exist across multiple marketing channels, so your attribution should too.

Previously brands have tried to close this gap by using various methods to map the offline customer journey. This usually took the form of a promotional code, which allows the brand to understand which channel had the desired effect. But whilst the picture is slightly clearer, it is not enough to be able to inform marketing budgets. Or to provide a clear understanding of the customer journey and the customer experience.

Only location intelligence can provide these insights. And it can do this across the online and offline world with a sufficient level of detail. Location data is versatile, quick and accurate. This makes it the perfect tool to help close the offline to online attribution loop.

Location data connect online advertising to the offline world. This allows for attribution in physical locations. This allows brands to measure store visits and link this offline activity to other digital touch points. It allows for more accurate customer journey mapping. This data can even be used to understand external offline touch points, such as OOH advertising. Already a complete picture becomes available.

Attribution has always had its problems. But brands and marketers should understand and implement insights from customer data points. In this way, location data provides a better understanding of the offline world. It allows brands to measure touch points more accurately. It allows them to map the customer journey in greater detail. And most of all, it allows them to measure the effects of cross-channel marketing in detail. 

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Location Data And Location Intelligence In 2018

The big data analytics space is growing at an alarming rate. We’re all increasingly connected through our mobile devices. This growth has provided huge amounts of data around audiences and how they behave. What kind of competitive advantage can we expect to see from this location data going forward?

Location data is rising in importance. But there are challenges that companies face as location data becomes commonplace. Businesses will need to understand the best application for this data. We’ll look at this as well as how location intelligence will be put to use across many verticals.

How will location data change the way that businesses approach marketing?


Location data will become invaluable to marketers as they look to close the online to offline attribution loop. Traditionally this has been a problem for marketers. It’s difficult to attribute real-world visits and purchases to online marketing. 

Location intelligence will help to illuminate what happens in the offline worldwith unprecedented accuracy. Big data will provide insights well beyond the capabilities of loyalty scenes.

As programming becomes more prevalent, location intelligence will be used to ensure that budgets are optimised. With the improvements in attribution, marketers will be able to accurately understand where ad fraud occurs.


Big data breeds personalization. Many a case has been made for the importance of big data in personalization. Location intelligence will help marketers to automate personalization. This, in turn, will deliver accurate, personalized content to the right user in the right place.

Big data insights from location data

Location data will provide a competitive advantage, and not just in marketing and advertising. More industries are realising the benefits of real-time mobile location data.

Location data is being collected and stored by over 90% of companies with over 500 employees. The applications of this data are wide-reaching. Understanding consumer behaviour accurately, and in real-time will signal the adoption of location intelligence outside of the marketing and advertising industries. Location data will allow cities to become smarter. It will allow transportation to become more efficient. 


Accuracy and reliability of big data

The accuracy problem

The problem has previously been the accuracy of the data. For companies to use big data to inform business decisions, the data has to be reliable beyond doubt. Location intelligence and precise sensor-driven data now provide this certainty. 

Accuracy and precision will become of paramount importance as technology gets better.  It’s important that businesses that benefit from location data and location intelligence can safely say that they are working from accurate sources. 

First-party location data

Challenges in sourcing accurate first-party data were found with scalability. With a lack of first-party data, unreliable third-party data was used. Often out of date and imprecise, the insights were not reliable. Especially for businesses serious about using location intelligence for commercial success.

Today Tamoco adopts a network approach to location intelligence. This means that through our mobile SDK we can understand mobile device location directly. Thanks to our network approach, we can understand these signals across multiple sensors types. 

This improves accuracy and will allow businesses to act based on a more precise understanding of audience behaviour. 

Real-time data

Location intelligent decisions must be informed by real-time data. Location data must be instantaneous. Data that is out of date is simply not useful for businesses. This means that data must be communicated in real-time to optimise and achieve the desired goals. 

This is another reason why first-party data is important. It allows for location intelligence to occur quickly.

The location of things

The IoT will soon be in everything. For example, by 2020 IoT technology will be in 95% of new product designs for electronics. Now that’s great but it’s only the beginning.

This huge growth will produce large amounts of data. However, it’s may be difficult to interpret data sets without location. Location adds context and a better understanding of what is happening in a specific location. This allows us to better understand the offline world. 

The location of things – the IoT is actually only useful if you know where the thing is. Tamoco’s sensory agnostic approach allows us to gain location intelligence across a wide range of sensors. These range from beacons, Wi-Fi and Geofences to connected IoT devices. This approach allows us to understand location across many different sensors. This creates a deeper understanding of the offline world. Businesses using location data will have to take a similar approach to understand the connected world we live in.

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Marketing & Advertising

How To Improve Mobile Targeting With Location Data

Mobile advertising is growing incredibly fast. Smartphone adoption has never been higher. Accordingly, marketers are adopting a mobile-first approach. As marketers look to target audiences on mobile devices, they will need to adapt to stay effective at targeting the right person, with the right message at the right time. 

Mobile targeting is set to pass other channels as more budget is being allocated to reaching audiences on the move. To stay ahead here’s how to improve mobile targeting and advertising using location data and today’s location based marketing technology. 


Location intelligence is an effective method to improve mobile targeting

Location data is a useful tool to help target mobile devices effectively. It allows in-depth segmentation of mobile audiences. It also facilitates real-time message delivery based on a device’s location.

This level of accurate data insight improves the effectiveness of mobile targeting. Location based marketing has been proven to increase engagement and conversion rates. This is because mobile targeting occurs contextually, at the best possible moment.

Location data is used to build complex mobile audiences profiles. This is achieved by understanding these anonymous location signals. Mobile targeting campaigns should feel personal and relevant to the user.  This is achieved by applying offline behaviour to the targeting process. 

Adding location intelligence to mobile targeting means less chance of delivering content that is irrelevant or annoying. In a world of increased ad-blocking, users want marketing to be helpful. Location based marketing allows mobile targeting campaigns to be just this. They reach users in the best moment with personalized and useful relevancy of the content


Personalize mobile content and combine this with relevant mobile targeting

Location intelligence allows brands and marketers to target users in real-time with accurate and personalized content. Ensure that mobile campaigns combine accurate targeting with contextual content relevant to the user’s location. Personalization should extend beyond simply addressing a user by name. It should be personalized to a level that reflects the location and situation of the mobile device. 

Personalization is key to successful mobile targeting. Improving the relevancy of the content will improve the success of campaigns. High-level mobile personalization is achieved with accurate location data. Understanding the situation of a mobile user allows content to be targeted and personalised. Users are more likely to interact with content that is targeted to their situation. 


Make sure your data is accurate and precise

Using location intelligence to target mobile users in the right micro-moment will improve campaign performance. Targeting devices at exactly the right time ensure that you will reach the user in the best place at the right moment. This requires the location data that fuels your campaign to be precise and instant.

Ensure that your location data partner that can source accurate data in real-time to inform your mobile targeting campaigns. Ask how your data is sourced. Using third-party data may not be helpful as it could be incorrectly sourced or out of date. 

There’s a lot of bad location data out there. Effective mobile targeting should be based on first-party data sourced from precise sensors. For example, at Tamoco, we source location signals across multiple different sensor types. This helps to understand device location with accuracy. It also ensures that errors in location data are not included in data sets. Data is also sourced from first-party sources. This means that there is little chance of data being out of date, or lose accuracy through the reselling of source data. 

Marketers should take accuracy and precision seriously. It means that mobile targeting campaign budgets are as efficient as possible. It ensures that brands and advertisers don’t waste time and money targeting the wrong audience. Each advertising dollar goes further as brands get better, more accurate mobile targeting. 


Apply mobile targeting and attribution across channels

Use location intelligence to understand if your mobile targeting is working. Location based marketing provides accurate attribution after devices are targeted. This allows marketers to understand campaign performance in the offline world. The aim of your targeted mobile campaign might be to send users to a specific store. It makes sense to measure the performance of this using location based attribution.

Many mobile marketers aren’t aware of the huge potential for location in closing the online to offline attribution loop. It can help brands to make better mobile targeting decisions over time.

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App Monetization Strategies – Which Is Best For My App?

The complete guide to mobile app monetization strategies. And, how to turn your app into a viable business.

Mobile app monetization in 2017

App monetization is pivotal to your mobile app business strategy. The revenue generated from your mobile app must outperform the cost of your user acquisition. If it doesn’t, then you’ll start to run into some serious problems.

That’s why it’s crucial to stay informed and to understand different app revenue models. One of the biggest challenges of creating a successful mobile app is figuring out which app monetization strategy best suits your app.

There are many different ways to monetize mobile apps. Ultimately, you need to figure out two things to form an effective app monetization strategy:

  • What value does your app provide to your app users and what is the price?

  • Which types of revenue will you pursue?

Understand your app monetization strategy in the context of your wider business model. We’ll talk more about this late on. For now, let’s look at some of the most common monetization strategies out there, as well as some that you might not have heard of.


No monetization strategy is the same

Your mobile monetization strategy is a constant balance between app revenue and user experience. In this balance lies the opportunity to propel your app to success. There are many theories on how to monetize an app effectively. But no particular method is inherently better than another. 

What’s the difference between android app monetization strategies and iOS app monetization solutions? Some app monetization models do provide different results on different platforms. It seems that iOS apps find it easier to monetize their audiences, but it isn’t exactly clear why this is.

As you might have realized by now, there’s no reason why you shouldn’t adopt different monetization strategies for your app. Make sure you’re aware of the effectiveness of each model on each platform. And always remember to put yourself in your user’s shoes when considering app monetization trends.

Anyway, let’s get onto the different app monetization strategies for your app.

It’s a topic that generates much debate amongst developers, users, and brands. But, for now, it’s here to stay. Mobile advertising has grown at an incredible rate in recent years. It’s a common response when people ask how to monetize free apps.

Mobile ad spend is set to reach $50 billion by the end of 2017. Mobile advertising as a percentage of digital advertising spend is also increasing year on year. This trend is predicted to continue.

Many apps head along the in-app advertising strategy. There are some app monetization challenges along the way. But, it can be one of the easiest and quickest way to monetize your audience.


In-app advertising

This is by far the most popular way for apps to generate extra revenue. Without advertising revenue, many apps wouldn’t exist.

There are a few ways that you can monetize apps with ads. There’s a huge variety in CPM and there are not that many guarantees on exact income until you get stuck-in. In-app ads are developing and aren’t as intrusive as they once were.

  • Third party ads within your app experience. These include everything from banner ads to third-party push notification advertising. Income can vary depending on your audience and your partner.

  • Build your own network of advertising space. This app monetization strategy requires a decent sized budget. Of course, once you manage to get to this stage you’ll reap the benefits of 100% of that ad revenue.

There is a lot of noise in the mobile ad space and it can be difficult to understand which solution is the best for your app. For a app network to advertise with, you’ll struggle to find a better list.


App push notification advertising.

You could be receiving significant income from your mobile app by allowing third-party brands to reach your users with third-party push notifications.

This monetization strategy is a two birds one stone solution to the monetization problem. If you ensure that the content delivered to the user is valuable, then you can also engage your users whilst monetizing your app.

With mobile monetization there will always be challenges. In-app advertisers often come across a pretty big one. 

Users hate ads.

There’s no getting around it. If you go for this kind of app monetization you are more or less signing up to decrease your UX.

Now if you have a mobile game app then this can be fine. But for other apps, it can become problematic. You need to think long and hard about your app’s function. Think how important experience is to the success of your app before implementing this monetization strategy.

You can always set limits to the amount of in-app advertising – many successful apps find a nice balance between the two.

  • Pros: quick and simple – pretty much any app can implement.
  • Cons: User experience is almost certainly lessened.


Monetize app data

One of the best ways to incorporate a free app monetization strategy is to leverage the huge amount of data generated from your mobile audience. It’s also another great way to monetize free apps without passing on the cost of the app directly to your users.

If you have built up a large mobile audience then big data around these user’s habits are extremely valuable for other companies. Their interests lie in understanding customers, and apps can help with this.

There’s huge potential for revenue here. This is a mobile app monetization trend that’s improving in popularity amongst developers.

CPMs are much higher than in-app advertising. There’s also the added benefit of not compromising your app experience. Data monetization takes place entirely in the background, ensuring that you can focus on improving the app experience. Rather than pestering users with in-app ads.

This method works well as a social app monetization strategy. But, it’s also effective revenue generator across most of the categories in the app store.

It’s important that you find yourself a valuable partner when embarking on this monetization strategy. You should ensure that your users are clearly opting into data monetization services. The key is explaining the process to your users. As with any mobile app monetization strategy, there’s always a trade-off for the user.

Ensuring an opt-out is important for any strategy. If done properly, this method can be one of the most lucrative app monetization templates.

  • Pros: best app monetization strategy for revenue and user experience
  • Cons: Requires reliable partner to implement.

Affiliate marketing and lead generation

Affiliate marketing is a method of app monetization that involves earning a healthy commission when your audience downloads, buys or engages with another product or service. If they do this through your app, then you’ll get generate revenue every time this happens.

This isn’t too dissimilar to mobile ads, and in some cases, it can appear just as blunt.


An affiliate in-app ad in cut the rope


But many other apps do this well and in these cases, it rarely affects the app experience negatively.

There are networks that help with affiliate marketing. The reward is much higher if you have the capacity to negotiate these partnerships yourself.

  • Pros: less negative effect on the user experience.
  • Cons: requires potentially lengthy relationship building with partners.


Transaction fees

Your app might include a marketplace or include many audience transactions. If this is the case then a transaction fee monetization will most likely be part of your app’s business plan already.

This app monetization strategy scales really nicely. Unlike listing fees, this encourages users to use your app service. The more transactions that go through your app, the more income you receive. That kind of information is helpful when forecasting app revenue and setting clear app growth KPIs.



Etsy charges app users a fee

  • Pros: Scalable and easy to predict monetization income
  • cons: Requires an engaged audience using your marketplace/service



The freemium app monetization model is one that has gained a lot of popularity in recent times.

The model is simple. You offer users a free, basic and useful version of your service. Simultaneously, you educate and entice your users to upgrade to the paid version with advanced features and capabilities.

The most common example of this app monetization is SaaS-based apps. 


Headspace makes certain features available to premium subscribers.


There are a few different freemium models:

  • Time-based – the users get the entire version of your app including all of its features. But, this lasts for a set period of time. This is similar to the free trial model that many non-app based SaaS providers offer.

  • Feature-based – in this app monetization model the user has access to only a select few features. To unlock the full range they must subscribe or upgrade their membership.

  • Limits – the user has access to all of the app features but they are given a usage limit. When they hit this limit they must upgrade their membership to keep using the app.

Some apps adopt just one of these methods but some have been known to mix two or even all these methods. Again, it really depends on which aspects of your app will entice your users to pay for the whole shebang.

  • Pros: Can be applied to the majority of mobile applications.

  • Cons: you must really focus on creating a useful and well-liked product.


Virtual currency

Generally one for those games apps out there but increasingly seen in other app categories. Many successful app monetization strategies involve offering a virtual currency to users. This can then be earned by playing/engaging with the app.

The users can use this currency to get ahead in the game or unlock certain app features and services. The user can then purchase this currency using real money.

You can really get into app economics here and there are many companies that offer expert advice on how to get the balance right. There’s actually a surprising amount of psychology involved.

Ultimately, you must understand your user’s motivations. As well as give audiences sufficient opportunity to earn the virtual currency themselves. Either way, there are a huge number of people that are willing to get their credit card out if what they are getting in return is good enough.

  • Pros: can provide large amounts of income and scales nicely.
  • Cons: Some of the app stores take a huge cut for in-purchases.


Which is the best app monetization platform?

Wouldn’t it be helpful if you could access these strategies all from within a single platform?

There are a few solutions for this – especially the mobile ad monetization platforms.

But some of the data platforms are quick to integrate, simple to use and provide you with a great overview of your mobile monetization strategy in real-time.

You can see exactly how your monetization strategy is performing. This allows you to forecast potential income streams. It also helps to predict how much of your mobile app budget you can allocate to other parts of your mobile business.


In conclusion

So that’s it, the main ways the world’s apps are generating money from their mobile app audience. There might not be a single best way to monetize your Android or iOS app. But there are many app monetization ideas out there to ensure that you get the best shot at generating app revenue for your business.

  • You must understand your audience to be able to monetize your app effectively.

  • No single monetization strategy will work on its own. The majority of apps looking to succeed as a viable business will consider all the options and implement more than one to their strategy.

  • Put your user first = if your app relies on a beautiful and seamless user experience then don’t overdo the ads.

  • Don’t expect overnight result – use feedback and data to understand and learn about your app and its users.

  • Every app is different – what works for your app might not work for others and vice-versa.

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